Load libraries
library(knitr)
library(rmdformats)
library(ggplot2)
library(ggpubr)
library(GGally)
library(car)
library(tidyverse)
library(lme4)
library(lmerTest)
library("MuMIn")
library(lmtest)
library(boot)
Read datasets
AllSubs_NeuralActivation <- read.csv('/Users/luisalvarez/Documents/GitHub/RM_Thesis_Neuroforecasting/ProcessedData/AllSubs_NeuralActivation_Aggregate_Combined_clean.csv')
AllSubs_NeuralActivation_Comedy <- read.csv('/Users/luisalvarez/Documents/GitHub/RM_Thesis_Neuroforecasting/ProcessedData/AllSubs_NeuralActivation_Aggregate_Combined_Comedy_clean.csv')
AllSubs_NeuralActivation_Horror <- read.csv('/Users/luisalvarez/Documents/GitHub/RM_Thesis_Neuroforecasting/ProcessedData/AllSubs_NeuralActivation_Aggregate_Combined_Horror_clean.csv')
Create data frames for each model.
# Define aggregate variables.
All_Gross_M1_log <- log(AllSubs_NeuralActivation$Gross_US_M1)
All_Theaters_M1 <- AllSubs_NeuralActivation$Theaters_US_M1
Comedy_Gross_M1_log <- log(AllSubs_NeuralActivation_Comedy$Gross_US_M1)
Comedy_Theaters_M1 <- AllSubs_NeuralActivation_Comedy$Theaters_US_M1
Horror_Gross_M1_log <- log(AllSubs_NeuralActivation_Horror$Gross_US_M1)
Horror_Theaters_M1 <- AllSubs_NeuralActivation_Horror$Theaters_US_M1
M1_df <- data.frame(All_Gross_M1_log, All_Theaters_M1)
M1_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_Theaters_M1)
M1_H_df <- data.frame(Horror_Gross_M1_log, Horror_Theaters_M1)
# Define affect variables.
All_PA <- AllSubs_NeuralActivation$Pos_arousal_scaled
All_NA <- AllSubs_NeuralActivation$Neg_arousal_scaled
Comedy_PA <- AllSubs_NeuralActivation_Comedy$Pos_arousal_scaled
Comedy_NA <- AllSubs_NeuralActivation_Comedy$Neg_arousal_scaled
Horror_PA <- AllSubs_NeuralActivation_Horror$Pos_arousal_scaled
Horror_NA <- AllSubs_NeuralActivation_Horror$Neg_arousal_scaled
M2_df <- data.frame(All_Gross_M1_log, All_PA, All_NA)
M2_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA)
M2_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA)
# Define ISC variables.
All_NAcc_ISC <- AllSubs_NeuralActivation$NAcc_ISC
All_AIns_ISC <- AllSubs_NeuralActivation$AIns_ISC
All_MPFC_ISC <- AllSubs_NeuralActivation$MPFC_ISC
Comedy_NAcc_ISC <- AllSubs_NeuralActivation_Comedy$NAcc_ISC
Comedy_AIns_ISC <- AllSubs_NeuralActivation_Comedy$AIns_ISC
Comedy_MPFC_ISC <- AllSubs_NeuralActivation_Comedy$MPFC_ISC
Horror_NAcc_ISC <- AllSubs_NeuralActivation_Horror$NAcc_ISC
Horror_AIns_ISC <- AllSubs_NeuralActivation_Horror$AIns_ISC
Horror_MPFC_ISC <- AllSubs_NeuralActivation_Horror$MPFC_ISC
# Define models.
M4_df <- data.frame(All_NAcc_ISC, All_AIns_ISC, All_MPFC_ISC)
M4_C_df <- data.frame(Comedy_NAcc_ISC, Comedy_AIns_ISC, Comedy_MPFC_ISC)
M4_H_df <- data.frame(Horror_NAcc_ISC, Horror_AIns_ISC, Horror_MPFC_ISC)
M5_df <- data.frame(All_Gross_M1_log, All_PA, All_NA, All_NAcc_ISC, All_AIns_ISC, All_MPFC_ISC)
M5_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_ISC, Comedy_AIns_ISC, Comedy_MPFC_ISC)
M5_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA, Horror_NAcc_ISC, Horror_AIns_ISC, Horror_MPFC_ISC)
# Define whole variables.
All_NAcc_whole <- AllSubs_NeuralActivation$NAcc_whole
All_AIns_whole <- AllSubs_NeuralActivation$AIns_whole
All_MPFC_whole <- AllSubs_NeuralActivation$MPFC_whole
Comedy_NAcc_whole <- AllSubs_NeuralActivation_Comedy$NAcc_whole
Comedy_AIns_whole <- AllSubs_NeuralActivation_Comedy$AIns_whole
Comedy_MPFC_whole <- AllSubs_NeuralActivation_Comedy$MPFC_whole
Horror_NAcc_whole <- AllSubs_NeuralActivation_Horror$NAcc_whole
Horror_AIns_whole <- AllSubs_NeuralActivation_Horror$AIns_whole
Horror_MPFC_whole <- AllSubs_NeuralActivation_Horror$MPFC_whole
# Define models.
M6_df <- data.frame(All_NAcc_whole, All_AIns_whole, All_MPFC_whole)
M6_C_df <- data.frame(Comedy_NAcc_whole, Comedy_AIns_whole, Comedy_MPFC_whole)
M6_H_df <- data.frame(Horror_NAcc_whole, Horror_AIns_whole, Horror_MPFC_whole)
M7_df <- data.frame(All_Gross_M1_log, All_PA, All_NA, All_NAcc_whole, All_AIns_whole, All_MPFC_whole)
M7_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_whole,
Comedy_AIns_whole, Comedy_MPFC_whole)
M7_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA, Horror_NAcc_whole,
Horror_AIns_whole, Horror_MPFC_whole)
# Define onset variables.
All_NAcc_onset <- AllSubs_NeuralActivation$NAcc_onset
All_AIns_onset <- AllSubs_NeuralActivation$AIns_onset
All_MPFC_onset <- AllSubs_NeuralActivation$MPFC_onset
Comedy_NAcc_onset <- AllSubs_NeuralActivation_Comedy$NAcc_onset
Comedy_AIns_onset <- AllSubs_NeuralActivation_Comedy$AIns_onset
Comedy_MPFC_onset <- AllSubs_NeuralActivation_Comedy$MPFC_onset
Horror_NAcc_onset <- AllSubs_NeuralActivation_Horror$NAcc_onset
Horror_AIns_onset <- AllSubs_NeuralActivation_Horror$AIns_onset
Horror_MPFC_onset <- AllSubs_NeuralActivation_Horror$MPFC_onset
# Define models.
M8_df <- data.frame(All_NAcc_onset, All_AIns_onset, All_MPFC_onset)
M8_C_df <- data.frame(Comedy_NAcc_onset, Comedy_AIns_onset, Comedy_MPFC_onset)
M8_H_df <- data.frame(Horror_NAcc_onset, Horror_AIns_onset, Horror_MPFC_onset)
M9_df <- data.frame(All_Gross_M1_log, All_PA, All_NA, All_NAcc_onset, All_AIns_onset, All_MPFC_onset)
M9_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_onset,
Comedy_AIns_onset, Comedy_MPFC_onset)
M9_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA, Horror_NAcc_onset,
Horror_AIns_onset, Horror_MPFC_onset)
# Define middle variables.
All_NAcc_middle <- AllSubs_NeuralActivation$NAcc_middle
All_AIns_middle <- AllSubs_NeuralActivation$AIns_middle
All_MPFC_middle <- AllSubs_NeuralActivation$MPFC_middle
Comedy_NAcc_middle <- AllSubs_NeuralActivation_Comedy$NAcc_middle
Comedy_AIns_middle <- AllSubs_NeuralActivation_Comedy$AIns_middle
Comedy_MPFC_middle <- AllSubs_NeuralActivation_Comedy$MPFC_middle
Horror_NAcc_middle <- AllSubs_NeuralActivation_Horror$NAcc_middle
Horror_AIns_middle <- AllSubs_NeuralActivation_Horror$AIns_middle
Horror_MPFC_middle <- AllSubs_NeuralActivation_Horror$MPFC_middle
# Define models.
M10_df <- data.frame(All_NAcc_middle, All_AIns_middle, All_MPFC_middle)
M10_C_df <- data.frame(Comedy_NAcc_middle, Comedy_AIns_middle, Comedy_MPFC_middle)
M10_H_df <- data.frame(Horror_NAcc_middle, Horror_AIns_middle, Horror_MPFC_middle)
M11_df <- data.frame(All_Gross_M1_log, All_PA, All_NA, All_NAcc_middle, All_AIns_middle, All_MPFC_middle)
M11_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_middle,
Comedy_AIns_middle, Comedy_MPFC_middle)
M11_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA, Horror_NAcc_middle,
Horror_AIns_middle, Horror_MPFC_middle)
# Define middle variables.
All_NAcc_offset <- AllSubs_NeuralActivation$NAcc_offset
All_AIns_offset <- AllSubs_NeuralActivation$AIns_offset
All_MPFC_offset <- AllSubs_NeuralActivation$MPFC_offset
Comedy_NAcc_offset <- AllSubs_NeuralActivation_Comedy$NAcc_offset
Comedy_AIns_offset <- AllSubs_NeuralActivation_Comedy$AIns_offset
Comedy_MPFC_offset <- AllSubs_NeuralActivation_Comedy$MPFC_offset
Horror_NAcc_offset <- AllSubs_NeuralActivation_Horror$NAcc_offset
Horror_AIns_offset <- AllSubs_NeuralActivation_Horror$AIns_offset
Horror_MPFC_offset <- AllSubs_NeuralActivation_Horror$MPFC_offset
# Define models.
M12_df <- data.frame(All_NAcc_offset, All_AIns_offset, All_MPFC_offset)
M12_C_df <- data.frame(Comedy_NAcc_offset, Comedy_AIns_offset, Comedy_MPFC_offset)
M12_H_df <- data.frame(Horror_NAcc_offset, Horror_AIns_offset, Horror_MPFC_offset)
M13_df <- data.frame(All_Gross_M1_log, All_PA, All_NA, All_NAcc_offset, All_AIns_offset, All_MPFC_offset)
M13_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_offset,
Comedy_AIns_offset, Comedy_MPFC_offset)
M13_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA, Horror_NAcc_offset,
Horror_AIns_offset, Horror_MPFC_offset)
M14_df <- data.frame(All_Gross_M1_log, All_PA, All_NA, All_NAcc_onset, All_AIns_middle, All_MPFC_offset)
M14_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_onset,
Comedy_AIns_middle, Comedy_MPFC_offset)
M14_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA, Horror_NAcc_onset,
Horror_AIns_middle, Horror_MPFC_offset)
Notes:
- Have note removed outliers from data.
Neuroforecasting: First Month US.
M1: Aggregste data
Call:
lm(formula = log(Gross_US_M1) ~ Type + +scale(Theaters_US_M1) +
Type:scale(Theaters_US_M1), data = AllSubs_NeuralActivation %>%
mutate(Type = factor(Type, levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-0.77903 -0.23205 -0.05965 0.21883 0.83396
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.21275 0.12503 137.673 < 2e-16 ***
Typecomedy -0.03297 0.16727 -0.197 0.845
scale(Theaters_US_M1) 0.96069 0.17747 5.413 1.13e-05 ***
Typecomedy:scale(Theaters_US_M1) -0.24037 0.20114 -1.195 0.243
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.4365 on 26 degrees of freedom
Multiple R-squared: 0.7846, Adjusted R-squared: 0.7597
F-statistic: 31.56 on 3 and 26 DF, p-value: 8.065e-09
R2m R2c
[1,] 0.7655209 0.7655209
[1] 41.10136



M2: Affective data alone
Call:
lm(formula = log(Gross_US_M1) ~ Type + scale(Pos_arousal_scaled) +
scale(Neg_arousal_scaled) + Type:scale(Pos_arousal_scaled) +
Type:scale(Neg_arousal_scaled), data = AllSubs_NeuralActivation %>%
mutate(Type = factor(Type, levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-1.2843 -0.6926 0.1338 0.4828 1.3591
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.6704 0.6299 28.051 <2e-16 ***
Typecomedy -1.6873 1.0543 -1.600 0.123
scale(Pos_arousal_scaled) -0.3907 0.4778 -0.818 0.422
scale(Neg_arousal_scaled) -0.5110 0.4545 -1.124 0.272
Typecomedy:scale(Pos_arousal_scaled) 0.7876 0.5368 1.467 0.155
Typecomedy:scale(Neg_arousal_scaled) -0.4148 1.0734 -0.386 0.703
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.845 on 24 degrees of freedom
Multiple R-squared: 0.2545, Adjusted R-squared: 0.09923
F-statistic: 1.639 on 5 and 24 DF, p-value: 0.188
R2m R2c
[1,] 0.2203203 0.2203203
[1] 82.33984



M3: Aggregate and affective data alone
Call:
lm(formula = log(Gross_US_M1) ~ Type + scale(Pos_arousal_scaled) +
scale(Neg_arousal_scaled) + Type:scale(Pos_arousal_scaled) +
Type:scale(Neg_arousal_scaled), data = AllSubs_NeuralActivation %>%
mutate(Type = factor(Type, levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-1.2843 -0.6926 0.1338 0.4828 1.3591
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.6704 0.6299 28.051 <2e-16 ***
Typecomedy -1.6873 1.0543 -1.600 0.123
scale(Pos_arousal_scaled) -0.3907 0.4778 -0.818 0.422
scale(Neg_arousal_scaled) -0.5110 0.4545 -1.124 0.272
Typecomedy:scale(Pos_arousal_scaled) 0.7876 0.5368 1.467 0.155
Typecomedy:scale(Neg_arousal_scaled) -0.4148 1.0734 -0.386 0.703
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.845 on 24 degrees of freedom
Multiple R-squared: 0.2545, Adjusted R-squared: 0.09923
F-statistic: 1.639 on 5 and 24 DF, p-value: 0.188
R2m R2c
[1,] 0.2203203 0.2203203
[1] 82.33984
M4: ISC data alone
Call:
lm(formula = log(Gross_US_M1) ~ Type + +scale(NAcc_ISC) + scale(AIns_ISC) +
scale(MPFC_ISC) + Type:scale(NAcc_ISC) + Type:scale(AIns_ISC) +
Type:scale(MPFC_ISC), data = AllSubs_NeuralActivation %>%
mutate(Type = factor(Type, levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-1.1415 -0.5179 -0.0290 0.3284 1.7190
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.35882 0.24765 70.093 <2e-16 ***
Typecomedy -0.32720 0.33377 -0.980 0.3376
scale(NAcc_ISC) 0.82609 0.36598 2.257 0.0343 *
scale(AIns_ISC) -0.23898 0.24217 -0.987 0.3345
scale(MPFC_ISC) 0.04594 0.36435 0.126 0.9008
Typecomedy:scale(NAcc_ISC) -0.87443 0.42880 -2.039 0.0536 .
Typecomedy:scale(AIns_ISC) 0.40309 0.41697 0.967 0.3442
Typecomedy:scale(MPFC_ISC) 0.12766 0.42544 0.300 0.7669
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8607 on 22 degrees of freedom
Multiple R-squared: 0.2911, Adjusted R-squared: 0.06551
F-statistic: 1.29 on 7 and 22 DF, p-value: 0.3005
R2m R2c
[1,] 0.2375059 0.2375059
[1] 84.832



M5: ISC data + affective data + behavioral data
Call:
lm(formula = log(Gross_US_M1) ~ Type + scale(Theaters_US_M1) +
scale(Pos_arousal_scaled) + scale(Neg_arousal_scaled) + scale(NAcc_ISC) +
scale(AIns_ISC) + scale(MPFC_ISC) + Type:scale(Theaters_US_M1) +
Type:scale(Pos_arousal_scaled) + Type:scale(Neg_arousal_scaled) +
Type:scale(NAcc_ISC) + Type:scale(AIns_ISC) + Type:scale(MPFC_ISC),
data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type,
levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-0.61447 -0.19931 -0.01218 0.17574 0.65657
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16.8771 0.3831 44.053 < 2e-16 ***
Typecomedy -0.1961 0.6399 -0.307 0.76316
scale(Theaters_US_M1) 0.9387 0.2874 3.266 0.00485 **
scale(Pos_arousal_scaled) -0.5317 0.2352 -2.261 0.03806 *
scale(Neg_arousal_scaled) -0.1696 0.2726 -0.622 0.54257
scale(NAcc_ISC) 0.1718 0.2617 0.657 0.52082
scale(AIns_ISC) -0.1537 0.1184 -1.298 0.21253
scale(MPFC_ISC) 0.4706 0.1961 2.400 0.02893 *
Typecomedy:scale(Theaters_US_M1) -0.2456 0.3124 -0.786 0.44335
Typecomedy:scale(Pos_arousal_scaled) 0.6018 0.3027 1.988 0.06415 .
Typecomedy:scale(Neg_arousal_scaled) -0.3782 0.6226 -0.607 0.55208
Typecomedy:scale(NAcc_ISC) -0.1277 0.3106 -0.411 0.68651
Typecomedy:scale(AIns_ISC) 0.0596 0.2458 0.242 0.81153
Typecomedy:scale(MPFC_ISC) -0.4412 0.2321 -1.901 0.07543 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.4051 on 16 degrees of freedom
Multiple R-squared: 0.8858, Adjusted R-squared: 0.7929
F-statistic: 9.543 on 13 and 16 DF, p-value: 3.208e-05
R2m R2c
[1,] 0.8105311 0.8105311
[1] 42.068



M6: Neural whole data alone
Call:
lm(formula = log(Gross_US_M1) ~ Type + +scale(NAcc_whole) + scale(AIns_whole) +
scale(MPFC_whole) + Type:scale(NAcc_whole) + Type:scale(AIns_whole) +
Type:scale(MPFC_whole), data = AllSubs_NeuralActivation %>%
mutate(Type = factor(Type, levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-1.32304 -0.50651 -0.08924 0.60106 1.97862
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.35384 0.33174 52.312 <2e-16 ***
Typecomedy 0.06926 0.47558 0.146 0.886
scale(NAcc_whole) -0.39315 0.29889 -1.315 0.202
scale(AIns_whole) 0.27509 0.35468 0.776 0.446
scale(MPFC_whole) 0.05432 0.31449 0.173 0.864
Typecomedy:scale(NAcc_whole) 0.33746 0.40993 0.823 0.419
Typecomedy:scale(AIns_whole) 0.37420 0.55585 0.673 0.508
Typecomedy:scale(MPFC_whole) -0.01654 0.38884 -0.043 0.966
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8986 on 22 degrees of freedom
Multiple R-squared: 0.2274, Adjusted R-squared: -0.01845
F-statistic: 0.9249 on 7 and 22 DF, p-value: 0.5067
R2m R2c
[1,] 0.182512 0.182512
[1] 87.41332



M7: Neural whole data + affective data + behavioral data
Call:
lm(formula = log(Gross_US_M1) ~ Type + scale(Theaters_US_M1) +
scale(Pos_arousal_scaled) + scale(Neg_arousal_scaled) + scale(NAcc_whole) +
scale(AIns_whole) + scale(MPFC_whole) + Type:scale(Theaters_US_M1) +
Type:scale(Pos_arousal_scaled) + Type:scale(Neg_arousal_scaled) +
Type:scale(NAcc_whole) + Type:scale(AIns_whole) + Type:scale(MPFC_whole),
data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type,
levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-0.77925 -0.23188 -0.03919 0.21060 0.71933
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16.75767 0.41552 40.330 < 2e-16 ***
Typecomedy -0.20478 0.70623 -0.290 0.775566
scale(Theaters_US_M1) 0.91243 0.21522 4.240 0.000625 ***
scale(Pos_arousal_scaled) -0.46120 0.39426 -1.170 0.259217
scale(Neg_arousal_scaled) 0.05169 0.31610 0.164 0.872150
scale(NAcc_whole) -0.18524 0.16086 -1.152 0.266439
scale(AIns_whole) 0.17901 0.19312 0.927 0.367723
scale(MPFC_whole) 0.11211 0.23464 0.478 0.639258
Typecomedy:scale(Theaters_US_M1) -0.30435 0.24944 -1.220 0.240103
Typecomedy:scale(Pos_arousal_scaled) 0.59979 0.42522 1.411 0.177531
Typecomedy:scale(Neg_arousal_scaled) -0.87950 0.75108 -1.171 0.258753
Typecomedy:scale(NAcc_whole) 0.10170 0.25257 0.403 0.692530
Typecomedy:scale(AIns_whole) 0.14450 0.33688 0.429 0.673697
Typecomedy:scale(MPFC_whole) -0.08881 0.26652 -0.333 0.743285
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.4551 on 16 degrees of freedom
Multiple R-squared: 0.8559, Adjusted R-squared: 0.7387
F-statistic: 7.308 on 13 and 16 DF, p-value: 0.000175
R2m R2c
[1,] 0.7661365 0.7661365
[1] 49.04303



M8: Neural onset data alone
Call:
lm(formula = log(Gross_US_M1) ~ Type + +scale(NAcc_onset) + scale(AIns_onset) +
scale(MPFC_onset) + Type:scale(NAcc_onset) + Type:scale(AIns_onset) +
Type:scale(MPFC_onset), data = AllSubs_NeuralActivation %>%
mutate(Type = factor(Type, levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-1.56852 -0.68099 0.03323 0.60789 1.56732
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.51995 0.27900 62.795 <2e-16 ***
Typecomedy -0.53256 0.37467 -1.421 0.169
scale(NAcc_onset) -0.27027 0.29988 -0.901 0.377
scale(AIns_onset) -0.05588 0.35386 -0.158 0.876
scale(MPFC_onset) 0.10611 0.30796 0.345 0.734
Typecomedy:scale(NAcc_onset) 0.62362 0.39292 1.587 0.127
Typecomedy:scale(AIns_onset) -0.08131 0.49394 -0.165 0.871
Typecomedy:scale(MPFC_onset) 0.21547 0.44127 0.488 0.630
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8807 on 22 degrees of freedom
Multiple R-squared: 0.2578, Adjusted R-squared: 0.02162
F-statistic: 1.092 on 7 and 22 DF, p-value: 0.4021
R2m R2c
[1,] 0.2085365 0.2085365
[1] 86.20888



M9: Neural onset data + affective data + behavioral data
Call:
lm(formula = log(Gross_US_M1) ~ Type + scale(Theaters_US_M1) +
scale(Pos_arousal_scaled) + scale(Neg_arousal_scaled) + scale(NAcc_onset) +
scale(AIns_onset) + scale(MPFC_onset) + Type:scale(Theaters_US_M1) +
Type:scale(Pos_arousal_scaled) + Type:scale(Neg_arousal_scaled) +
Type:scale(NAcc_onset) + Type:scale(AIns_onset) + Type:scale(MPFC_onset),
data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type,
levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-0.48991 -0.18987 -0.01771 0.20364 0.57552
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.43628 0.42827 40.713 < 2e-16 ***
Typecomedy -0.52314 0.64739 -0.808 0.4309
scale(Theaters_US_M1) 0.97894 0.17928 5.460 5.24e-05 ***
scale(Pos_arousal_scaled) -0.41513 0.27486 -1.510 0.1505
scale(Neg_arousal_scaled) -0.31982 0.29709 -1.077 0.2977
scale(NAcc_onset) -0.29130 0.13192 -2.208 0.0422 *
scale(AIns_onset) -0.45891 0.20477 -2.241 0.0396 *
scale(MPFC_onset) 0.19915 0.16578 1.201 0.2471
Typecomedy:scale(Theaters_US_M1) -0.28279 0.20766 -1.362 0.1921
Typecomedy:scale(Pos_arousal_scaled) 0.51509 0.30803 1.672 0.1139
Typecomedy:scale(Neg_arousal_scaled) 0.03968 0.59659 0.067 0.9478
Typecomedy:scale(NAcc_onset) 0.34909 0.19837 1.760 0.0975 .
Typecomedy:scale(AIns_onset) 0.53383 0.26202 2.037 0.0585 .
Typecomedy:scale(MPFC_onset) -0.31756 0.22934 -1.385 0.1852
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.3864 on 16 degrees of freedom
Multiple R-squared: 0.8961, Adjusted R-squared: 0.8117
F-statistic: 10.62 on 13 and 16 DF, p-value: 1.587e-05
R2m R2c
[1,] 0.8263552 0.8263552
[1] 39.21994



M10: Neural middle data alone
Call:
lm(formula = log(Gross_US_M1) ~ Type + +scale(NAcc_middle) +
scale(AIns_middle) + scale(MPFC_middle) + Type:scale(NAcc_middle) +
Type:scale(AIns_middle) + Type:scale(MPFC_middle), data = AllSubs_NeuralActivation %>%
mutate(Type = factor(Type, levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-1.34877 -0.46630 0.05791 0.35276 1.35943
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.54254 0.26717 65.661 <2e-16 ***
Typecomedy -0.22617 0.36944 -0.612 0.5467
scale(NAcc_middle) -0.23600 0.30363 -0.777 0.4453
scale(AIns_middle) -0.02164 0.26157 -0.083 0.9348
scale(MPFC_middle) -0.32698 0.23354 -1.400 0.1754
Typecomedy:scale(NAcc_middle) -0.24320 0.37827 -0.643 0.5269
Typecomedy:scale(AIns_middle) 0.76464 0.41771 1.831 0.0807 .
Typecomedy:scale(MPFC_middle) 0.61956 0.34308 1.806 0.0846 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.7989 on 22 degrees of freedom
Multiple R-squared: 0.3893, Adjusted R-squared: 0.195
F-statistic: 2.003 on 7 and 22 DF, p-value: 0.1008
R2m R2c
[1,] 0.3259475 0.3259475
[1] 80.35859



M11: Neural middle data + affective data + behavioral data
Call:
lm(formula = log(Gross_US_M1) ~ Type + scale(Theaters_US_M1) +
scale(Pos_arousal_scaled) + scale(Neg_arousal_scaled) + scale(NAcc_middle) +
scale(AIns_middle) + scale(MPFC_middle) + Type:scale(Theaters_US_M1) +
Type:scale(Pos_arousal_scaled) + Type:scale(Neg_arousal_scaled) +
Type:scale(NAcc_middle) + Type:scale(AIns_middle) + Type:scale(MPFC_middle),
data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type,
levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-0.56344 -0.22727 -0.01317 0.23050 0.83789
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16.76646 0.41163 40.732 < 2e-16 ***
Typecomedy -0.12802 0.63884 -0.200 0.843697
scale(Theaters_US_M1) 1.19746 0.29167 4.106 0.000827 ***
scale(Pos_arousal_scaled) -0.38338 0.27752 -1.381 0.186132
scale(Neg_arousal_scaled) -0.01356 0.28656 -0.047 0.962832
scale(NAcc_middle) 0.20506 0.22071 0.929 0.366648
scale(AIns_middle) 0.10961 0.15521 0.706 0.490226
scale(MPFC_middle) 0.12013 0.16695 0.720 0.482197
Typecomedy:scale(Theaters_US_M1) -0.60179 0.31897 -1.887 0.077485 .
Typecomedy:scale(Pos_arousal_scaled) 0.46453 0.32517 1.429 0.172355
Typecomedy:scale(Neg_arousal_scaled) -0.63757 0.70604 -0.903 0.379907
Typecomedy:scale(NAcc_middle) -0.32543 0.26528 -1.227 0.237669
Typecomedy:scale(AIns_middle) 0.10602 0.32074 0.331 0.745287
Typecomedy:scale(MPFC_middle) -0.02328 0.23194 -0.100 0.921293
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.4579 on 16 degrees of freedom
Multiple R-squared: 0.8541, Adjusted R-squared: 0.7356
F-statistic: 7.205 on 13 and 16 DF, p-value: 0.0001909
R2m R2c
[1,] 0.7635821 0.7635821
[1] 49.40732



M12: Neural offset data alone
Call:
lm(formula = log(Gross_US_M1) ~ Type + +scale(NAcc_offset) +
scale(AIns_offset) + scale(MPFC_offset) + Type:scale(NAcc_offset) +
Type:scale(AIns_offset) + Type:scale(MPFC_offset), data = AllSubs_NeuralActivation %>%
mutate(Type = factor(Type, levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-1.68479 -0.52415 -0.00219 0.37082 1.68291
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.38774 0.25875 67.199 <2e-16 ***
Typecomedy -0.46246 0.36823 -1.256 0.222
scale(NAcc_offset) -0.25394 0.26854 -0.946 0.355
scale(AIns_offset) 0.14645 0.24484 0.598 0.556
scale(MPFC_offset) 0.29161 0.36812 0.792 0.437
Typecomedy:scale(NAcc_offset) 0.08375 0.42881 0.195 0.847
Typecomedy:scale(AIns_offset) -0.35390 0.45922 -0.771 0.449
Typecomedy:scale(MPFC_offset) -0.52509 0.43920 -1.196 0.245
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8935 on 22 degrees of freedom
Multiple R-squared: 0.236, Adjusted R-squared: -0.007119
F-statistic: 0.9707 on 7 and 22 DF, p-value: 0.4762
R2m R2c
[1,] 0.1898313 0.1898313
[1] 87.07755



M13: Neural offset data + affective data + behavioral data
Call:
lm(formula = log(Gross_US_M1) ~ Type + scale(Theaters_US_M1) +
scale(Pos_arousal_scaled) + scale(Neg_arousal_scaled) + scale(NAcc_offset) +
scale(AIns_offset) + scale(MPFC_offset) + Type:scale(Theaters_US_M1) +
Type:scale(Pos_arousal_scaled) + Type:scale(Neg_arousal_scaled) +
Type:scale(NAcc_offset) + Type:scale(AIns_offset) + Type:scale(MPFC_offset),
data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type,
levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-0.70359 -0.25579 -0.00128 0.23431 0.90958
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16.99216 0.48505 35.031 < 2e-16 ***
Typecomedy -0.33549 0.69805 -0.481 0.63729
scale(Theaters_US_M1) 1.03416 0.34782 2.973 0.00897 **
scale(Pos_arousal_scaled) -0.27465 0.37154 -0.739 0.47048
scale(Neg_arousal_scaled) -0.05170 0.48217 -0.107 0.91594
scale(NAcc_offset) -0.02308 0.15177 -0.152 0.88103
scale(AIns_offset) 0.15293 0.16318 0.937 0.36259
scale(MPFC_offset) -0.13002 0.37376 -0.348 0.73247
Typecomedy:scale(Theaters_US_M1) -0.34235 0.36972 -0.926 0.36821
Typecomedy:scale(Pos_arousal_scaled) 0.40135 0.40090 1.001 0.33166
Typecomedy:scale(Neg_arousal_scaled) -0.53165 0.75770 -0.702 0.49297
Typecomedy:scale(NAcc_offset) 0.01871 0.23753 0.079 0.93819
Typecomedy:scale(AIns_offset) 0.01262 0.27181 0.046 0.96354
Typecomedy:scale(MPFC_offset) 0.10923 0.39725 0.275 0.78687
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.4701 on 16 degrees of freedom
Multiple R-squared: 0.8462, Adjusted R-squared: 0.7213
F-statistic: 6.772 on 13 and 16 DF, p-value: 0.0002785
R2m R2c
[1,] 0.7522186 0.7522186
[1] 50.98713



M14: Sequence Model
Call:
lm(formula = log(Gross_US_M1) ~ Type + scale(Theaters_US_M1) +
scale(NAcc_onset) + scale(AIns_middle) + scale(MPFC_offset) +
Type:scale(NAcc_onset) + Type:scale(AIns_middle) + Type:scale(MPFC_offset),
data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type,
levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-0.51548 -0.28740 -0.01475 0.22436 0.71228
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.14747 0.13408 127.894 < 2e-16 ***
Typecomedy 0.08844 0.18304 0.483 0.63398
scale(Theaters_US_M1) 0.75017 0.09635 7.786 1.27e-07 ***
scale(NAcc_onset) -0.45970 0.15936 -2.885 0.00887 **
scale(AIns_middle) 0.27372 0.13328 2.054 0.05267 .
scale(MPFC_offset) 0.16114 0.18491 0.871 0.39336
Typecomedy:scale(NAcc_onset) 0.57168 0.19161 2.984 0.00708 **
Typecomedy:scale(AIns_middle) -0.20753 0.20318 -1.021 0.31867
Typecomedy:scale(MPFC_offset) -0.11575 0.21721 -0.533 0.59970
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.4037 on 21 degrees of freedom
Multiple R-squared: 0.8511, Adjusted R-squared: 0.7944
F-statistic: 15.01 on 8 and 21 DF, p-value: 4.313e-07
R2m R2c
[1,] 0.8054243 0.8054243
[1] 40.01623
M15: Sequence Model 2
Call:
lm(formula = log(Gross_US_M1) ~ Type + scale(Theaters_US_M1) +
scale(Pos_arousal_scaled) + scale(Neg_arousal_scaled) + scale(NAcc_onset) +
scale(AIns_middle) + scale(MPFC_offset) + Type:scale(Theaters_US_M1) +
Type:scale(Pos_arousal_scaled) + Type:scale(Neg_arousal_scaled) +
Type:scale(NAcc_onset) + Type:scale(AIns_middle) + Type:scale(MPFC_offset),
data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type,
levels = c("horror", "comedy"))))
Residuals:
Min 1Q Median 3Q Max
-0.63173 -0.19482 0.00327 0.24375 0.48963
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.13406 0.37404 45.808 <2e-16 ***
Typecomedy -0.45999 0.61553 -0.747 0.4657
scale(Theaters_US_M1) 0.77611 0.29808 2.604 0.0192 *
scale(Pos_arousal_scaled) -0.40196 0.31210 -1.288 0.2161
scale(Neg_arousal_scaled) -0.30346 0.38099 -0.796 0.4374
scale(NAcc_onset) -0.48664 0.17457 -2.788 0.0132 *
scale(AIns_middle) 0.27343 0.13904 1.967 0.0668 .
scale(MPFC_offset) 0.39167 0.36012 1.088 0.2929
Typecomedy:scale(Theaters_US_M1) -0.18290 0.32165 -0.569 0.5775
Typecomedy:scale(Pos_arousal_scaled) 0.39576 0.35109 1.127 0.2763
Typecomedy:scale(Neg_arousal_scaled) -0.41113 0.72084 -0.570 0.5764
Typecomedy:scale(NAcc_onset) 0.55595 0.21261 2.615 0.0188 *
Typecomedy:scale(AIns_middle) -0.04367 0.25202 -0.173 0.8646
Typecomedy:scale(MPFC_offset) -0.44611 0.37969 -1.175 0.2572
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.3894 on 16 degrees of freedom
Multiple R-squared: 0.8945, Adjusted R-squared: 0.8087
F-statistic: 10.43 on 13 and 16 DF, p-value: 1.783e-05
R2m R2c
[1,] 0.8238359 0.8238359
[1] 39.68882



---
title: "R Notebook"
output: html_notebook
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

# Load libraries
```{r}
library(knitr)
library(rmdformats)
library(ggplot2)
library(ggpubr)
library(GGally)
library(car)
```


```{r, warning = FALSE, message = FALSE}
library(tidyverse)
library(lme4)
library(lmerTest)
library("MuMIn")
library(lmtest)
library(boot)
```

# Read datasets
```{r}
AllSubs_NeuralActivation <- read.csv('/Users/luisalvarez/Documents/GitHub/RM_Thesis_Neuroforecasting/ProcessedData/AllSubs_NeuralActivation_Aggregate_Combined_clean.csv')

AllSubs_NeuralActivation_Comedy <- read.csv('/Users/luisalvarez/Documents/GitHub/RM_Thesis_Neuroforecasting/ProcessedData/AllSubs_NeuralActivation_Aggregate_Combined_Comedy_clean.csv')

AllSubs_NeuralActivation_Horror <- read.csv('/Users/luisalvarez/Documents/GitHub/RM_Thesis_Neuroforecasting/ProcessedData/AllSubs_NeuralActivation_Aggregate_Combined_Horror_clean.csv')

```


# Create data frames for each model.
```{r}
# Define aggregate variables. 
All_Gross_M1_log <- log(AllSubs_NeuralActivation$Gross_US_M1)
All_Theaters_M1 <- AllSubs_NeuralActivation$Theaters_US_M1

Comedy_Gross_M1_log <- log(AllSubs_NeuralActivation_Comedy$Gross_US_M1)
Comedy_Theaters_M1 <- AllSubs_NeuralActivation_Comedy$Theaters_US_M1

Horror_Gross_M1_log <- log(AllSubs_NeuralActivation_Horror$Gross_US_M1)
Horror_Theaters_M1 <- AllSubs_NeuralActivation_Horror$Theaters_US_M1
  
M1_df <- data.frame(All_Gross_M1_log, All_Theaters_M1) 
M1_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_Theaters_M1) 
M1_H_df <- data.frame(Horror_Gross_M1_log, Horror_Theaters_M1) 

# Define affect variables.
All_PA <- AllSubs_NeuralActivation$Pos_arousal_scaled
All_NA <- AllSubs_NeuralActivation$Neg_arousal_scaled

Comedy_PA <- AllSubs_NeuralActivation_Comedy$Pos_arousal_scaled
Comedy_NA <- AllSubs_NeuralActivation_Comedy$Neg_arousal_scaled

Horror_PA <- AllSubs_NeuralActivation_Horror$Pos_arousal_scaled
Horror_NA <- AllSubs_NeuralActivation_Horror$Neg_arousal_scaled

M2_df <- data.frame(All_Gross_M1_log, All_PA, All_NA) 
M2_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA) 
M2_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA) 
```

```{r}
# Define ISC variables. 
All_NAcc_ISC <- AllSubs_NeuralActivation$NAcc_ISC
All_AIns_ISC <- AllSubs_NeuralActivation$AIns_ISC
All_MPFC_ISC <- AllSubs_NeuralActivation$MPFC_ISC

Comedy_NAcc_ISC <- AllSubs_NeuralActivation_Comedy$NAcc_ISC
Comedy_AIns_ISC <- AllSubs_NeuralActivation_Comedy$AIns_ISC
Comedy_MPFC_ISC <- AllSubs_NeuralActivation_Comedy$MPFC_ISC

Horror_NAcc_ISC <- AllSubs_NeuralActivation_Horror$NAcc_ISC
Horror_AIns_ISC <- AllSubs_NeuralActivation_Horror$AIns_ISC
Horror_MPFC_ISC <- AllSubs_NeuralActivation_Horror$MPFC_ISC

# Define models. 
M4_df <- data.frame(All_NAcc_ISC, All_AIns_ISC, All_MPFC_ISC) 
M4_C_df <- data.frame(Comedy_NAcc_ISC, Comedy_AIns_ISC, Comedy_MPFC_ISC) 
M4_H_df <- data.frame(Horror_NAcc_ISC, Horror_AIns_ISC, Horror_MPFC_ISC) 

M5_df <- data.frame(All_Gross_M1_log, All_PA, All_NA, All_NAcc_ISC, All_AIns_ISC, All_MPFC_ISC) 
M5_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_ISC, Comedy_AIns_ISC, Comedy_MPFC_ISC) 
M5_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA, Horror_NAcc_ISC, Horror_AIns_ISC, Horror_MPFC_ISC) 
```

```{r}
# Define whole variables. 
All_NAcc_whole <- AllSubs_NeuralActivation$NAcc_whole
All_AIns_whole <- AllSubs_NeuralActivation$AIns_whole
All_MPFC_whole <- AllSubs_NeuralActivation$MPFC_whole

Comedy_NAcc_whole <- AllSubs_NeuralActivation_Comedy$NAcc_whole
Comedy_AIns_whole <- AllSubs_NeuralActivation_Comedy$AIns_whole
Comedy_MPFC_whole <- AllSubs_NeuralActivation_Comedy$MPFC_whole

Horror_NAcc_whole <- AllSubs_NeuralActivation_Horror$NAcc_whole
Horror_AIns_whole <- AllSubs_NeuralActivation_Horror$AIns_whole
Horror_MPFC_whole <- AllSubs_NeuralActivation_Horror$MPFC_whole

# Define models. 
M6_df <- data.frame(All_NAcc_whole, All_AIns_whole, All_MPFC_whole) 
M6_C_df <- data.frame(Comedy_NAcc_whole, Comedy_AIns_whole, Comedy_MPFC_whole) 
M6_H_df <- data.frame(Horror_NAcc_whole, Horror_AIns_whole, Horror_MPFC_whole) 

M7_df <- data.frame(All_Gross_M1_log, All_PA, All_NA, All_NAcc_whole, All_AIns_whole, All_MPFC_whole) 
M7_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_whole,
                      Comedy_AIns_whole, Comedy_MPFC_whole) 
M7_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA, Horror_NAcc_whole,
                      Horror_AIns_whole, Horror_MPFC_whole) 
```

```{r}
# Define onset variables. 
All_NAcc_onset <- AllSubs_NeuralActivation$NAcc_onset
All_AIns_onset <- AllSubs_NeuralActivation$AIns_onset
All_MPFC_onset <- AllSubs_NeuralActivation$MPFC_onset

Comedy_NAcc_onset <- AllSubs_NeuralActivation_Comedy$NAcc_onset
Comedy_AIns_onset <- AllSubs_NeuralActivation_Comedy$AIns_onset
Comedy_MPFC_onset <- AllSubs_NeuralActivation_Comedy$MPFC_onset

Horror_NAcc_onset <- AllSubs_NeuralActivation_Horror$NAcc_onset
Horror_AIns_onset <- AllSubs_NeuralActivation_Horror$AIns_onset
Horror_MPFC_onset <- AllSubs_NeuralActivation_Horror$MPFC_onset

# Define models. 
M8_df <- data.frame(All_NAcc_onset, All_AIns_onset, All_MPFC_onset) 
M8_C_df <- data.frame(Comedy_NAcc_onset, Comedy_AIns_onset, Comedy_MPFC_onset) 
M8_H_df <- data.frame(Horror_NAcc_onset, Horror_AIns_onset, Horror_MPFC_onset) 

M9_df <- data.frame(All_Gross_M1_log, All_PA, All_NA, All_NAcc_onset, All_AIns_onset, All_MPFC_onset) 
M9_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_onset,
                      Comedy_AIns_onset, Comedy_MPFC_onset) 
M9_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA, Horror_NAcc_onset,
                      Horror_AIns_onset, Horror_MPFC_onset) 
```

```{r}
# Define middle variables. 
All_NAcc_middle <- AllSubs_NeuralActivation$NAcc_middle
All_AIns_middle <- AllSubs_NeuralActivation$AIns_middle
All_MPFC_middle <- AllSubs_NeuralActivation$MPFC_middle

Comedy_NAcc_middle <- AllSubs_NeuralActivation_Comedy$NAcc_middle
Comedy_AIns_middle <- AllSubs_NeuralActivation_Comedy$AIns_middle
Comedy_MPFC_middle <- AllSubs_NeuralActivation_Comedy$MPFC_middle

Horror_NAcc_middle <- AllSubs_NeuralActivation_Horror$NAcc_middle
Horror_AIns_middle <- AllSubs_NeuralActivation_Horror$AIns_middle
Horror_MPFC_middle <- AllSubs_NeuralActivation_Horror$MPFC_middle

# Define models. 
M10_df <- data.frame(All_NAcc_middle, All_AIns_middle, All_MPFC_middle) 
M10_C_df <- data.frame(Comedy_NAcc_middle, Comedy_AIns_middle, Comedy_MPFC_middle) 
M10_H_df <- data.frame(Horror_NAcc_middle, Horror_AIns_middle, Horror_MPFC_middle) 

M11_df <- data.frame(All_Gross_M1_log, All_PA, All_NA, All_NAcc_middle, All_AIns_middle, All_MPFC_middle) 
M11_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_middle,
                      Comedy_AIns_middle, Comedy_MPFC_middle) 
M11_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA, Horror_NAcc_middle,
                      Horror_AIns_middle, Horror_MPFC_middle) 
```

```{r}
# Define offset variables. 
All_NAcc_offset <- AllSubs_NeuralActivation$NAcc_offset
All_AIns_offset <- AllSubs_NeuralActivation$AIns_offset
All_MPFC_offset <- AllSubs_NeuralActivation$MPFC_offset

Comedy_NAcc_offset <- AllSubs_NeuralActivation_Comedy$NAcc_offset
Comedy_AIns_offset <- AllSubs_NeuralActivation_Comedy$AIns_offset
Comedy_MPFC_offset <- AllSubs_NeuralActivation_Comedy$MPFC_offset

Horror_NAcc_offset <- AllSubs_NeuralActivation_Horror$NAcc_offset
Horror_AIns_offset <- AllSubs_NeuralActivation_Horror$AIns_offset
Horror_MPFC_offset <- AllSubs_NeuralActivation_Horror$MPFC_offset

# Define models. 
M12_df <- data.frame(All_NAcc_offset, All_AIns_offset, All_MPFC_offset) 
M12_C_df <- data.frame(Comedy_NAcc_offset, Comedy_AIns_offset, Comedy_MPFC_offset) 
M12_H_df <- data.frame(Horror_NAcc_offset, Horror_AIns_offset, Horror_MPFC_offset) 

M13_df <- data.frame(All_Gross_M1_log, All_PA, All_NA, All_NAcc_offset, All_AIns_offset, All_MPFC_offset) 
M13_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_offset,
                      Comedy_AIns_offset, Comedy_MPFC_offset) 
M13_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA, Horror_NAcc_offset,
                      Horror_AIns_offset, Horror_MPFC_offset) 
```

```{r}

M14_df <- data.frame(All_Gross_M1_log, All_PA, All_NA, All_NAcc_onset, All_AIns_middle, All_MPFC_offset) 
M14_C_df <- data.frame(Comedy_Gross_M1_log, Comedy_PA, Comedy_NA, Comedy_NAcc_onset,
                      Comedy_AIns_middle, Comedy_MPFC_offset) 
M14_H_df <- data.frame(Horror_Gross_M1_log, Horror_PA, Horror_NA, Horror_NAcc_onset,
                      Horror_AIns_middle, Horror_MPFC_offset) 
```

# Notes: 
 - Have note removed outliers from data.

# Neuroforecasting: First Month US.
## M1: Aggregste data 
```{r, echo = FALSE}
M1 <- lm(log(Gross_US_M1) ~ Type +
         + scale(Theaters_US_M1)
         #+ Weeks_avg_per_theater
         + Type:scale(Theaters_US_M1)
         , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M1)
r.squaredGLMM(M1)
AIC(M1)

# Create pairs plot. 
ggpairs(M1_df)
ggpairs(M1_C_df)
ggpairs(M1_H_df)
```



## M2: Affective data alone
```{r, echo = FALSE}
M2 <- lm(log(Gross_US_M1) ~ Type 
         + scale(Pos_arousal_scaled) 
         + scale(Neg_arousal_scaled)
         + Type:scale(Pos_arousal_scaled)
         + Type:scale(Neg_arousal_scaled)
         , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M2)
r.squaredGLMM(M2)
AIC(M2)

# Create pairs plot. 
ggpairs(M2_df)
ggpairs(M2_C_df)
ggpairs(M2_H_df)
```

## M3: Aggregate and affective data alone
```{r, echo = FALSE}
M3 <- lm(log(Gross_US_M1) ~ Type 
         #+ scale(Theaters_US_M1)
         + scale(Pos_arousal_scaled) 
         + scale(Neg_arousal_scaled)
         #+ Type:scale(Theaters_US_M1)
         + Type:scale(Pos_arousal_scaled)
         + Type:scale(Neg_arousal_scaled)
         , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M3)
r.squaredGLMM(M3)
AIC(M3)
```

# M4: ISC data alone
```{r, echo = FALSE}
M4 <- lm(log(Gross_US_M1) ~ Type + 
              + scale(NAcc_ISC) 
              + scale(AIns_ISC) 
              + scale(MPFC_ISC) 
              + Type:scale(NAcc_ISC) 
              + Type:scale(AIns_ISC) 
              + Type:scale(MPFC_ISC) 
              , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M4)
r.squaredGLMM(M4)
AIC(M4)

# Create pairs plot. 
ggpairs(M4_df)
ggpairs(M4_C_df)
ggpairs(M4_H_df)
```

# M5: ISC data + affective data + behavioral data
```{r, echo = FALSE}
M5 <- lm(log(Gross_US_M1) ~ Type 
             + scale(Theaters_US_M1) 
             + scale(Pos_arousal_scaled) 
             + scale(Neg_arousal_scaled)  
             #+ scale(W_score_scaled) 
             + scale(NAcc_ISC) 
             + scale(AIns_ISC) 
             + scale(MPFC_ISC) 
             + Type:scale(Theaters_US_M1) 
             + Type:scale(Pos_arousal_scaled)
             + Type:scale(Neg_arousal_scaled)
             #+ Type:scale(W_score_scaled)
             + Type:scale(NAcc_ISC) 
             + Type:scale(AIns_ISC) 
             + Type:scale(MPFC_ISC)
             , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M5)
r.squaredGLMM(M5)
AIC(M5)

# Create pairs plot. 
ggpairs(M5_df)
ggpairs(M5_C_df)
ggpairs(M5_H_df)
```

# M6: Neural whole data alone
```{r, echo = FALSE}
M6 <- lm(log(Gross_US_M1) ~ Type + 
              #+ Theaters_US_W1_num 
              + scale(NAcc_whole) 
              + scale(AIns_whole) 
              + scale(MPFC_whole) 
              + Type:scale(NAcc_whole) 
              + Type:scale(AIns_whole) 
              + Type:scale(MPFC_whole) 
              , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M6)
r.squaredGLMM(M6)
AIC(M6)

# Create pairs plot. 
ggpairs(M6_df)
ggpairs(M6_C_df)
ggpairs(M6_H_df)
```

# M7: Neural whole data + affective data + behavioral data
```{r, echo = FALSE}
M7 <- lm(log(Gross_US_M1) ~ Type 
             + scale(Theaters_US_M1)
             + scale(Pos_arousal_scaled) 
             + scale(Neg_arousal_scaled)  
             + scale(NAcc_whole) 
             + scale(AIns_whole) 
             + scale(MPFC_whole) 
             + Type:scale(Theaters_US_M1)
             + Type:scale(Pos_arousal_scaled)
             + Type:scale(Neg_arousal_scaled)
             + Type:scale(NAcc_whole) 
             + Type:scale(AIns_whole) 
             + Type:scale(MPFC_whole)
             , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M7)
r.squaredGLMM(M7)
AIC(M7)

# Create pairs plot. 
ggpairs(M7_df)
ggpairs(M7_C_df)
ggpairs(M7_H_df)
```

# M8: Neural onset data alone
```{r, echo = FALSE}
M8 <- lm(log(Gross_US_M1) ~ Type + 
              + scale(NAcc_onset) 
              + scale(AIns_onset) 
              + scale(MPFC_onset) 
              + Type:scale(NAcc_onset) 
              + Type:scale(AIns_onset) 
              + Type:scale(MPFC_onset) 
              , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M8)
r.squaredGLMM(M8)
AIC(M8)

# Create pairs plot. 
ggpairs(M8_df)
ggpairs(M8_C_df)
ggpairs(M8_H_df)
```

# M9: Neural onset data + affective data + behavioral data
```{r, echo = FALSE}
M9 <- lm(log(Gross_US_M1) ~ Type 
             + scale(Theaters_US_M1)
             #+ Total_weeks 
             #+ Weeks_avg_per_theater
             + scale(Pos_arousal_scaled) 
             + scale(Neg_arousal_scaled)  
             #+ scale(W_score_scaled) 
             + scale(NAcc_onset) 
             + scale(AIns_onset) 
             + scale(MPFC_onset) 
             + Type:scale(Theaters_US_M1)
             + Type:scale(Pos_arousal_scaled)
             + Type:scale(Neg_arousal_scaled)
             #+ Type:scale(W_score_scaled)
             + Type:scale(NAcc_onset) 
             + Type:scale(AIns_onset) 
             + Type:scale(MPFC_onset)
             , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M9)
r.squaredGLMM(M9)
AIC(M9)

# Create pairs plot. 
ggpairs(M9_df)
ggpairs(M9_C_df)
ggpairs(M9_H_df)
```

# M10: Neural middle data alone
```{r, echo = FALSE}
M10 <- lm(log(Gross_US_M1) ~ Type + 
              + scale(NAcc_middle) 
              + scale(AIns_middle) 
              + scale(MPFC_middle) 
              + Type:scale(NAcc_middle) 
              + Type:scale(AIns_middle) 
              + Type:scale(MPFC_middle) 
              , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M10)
r.squaredGLMM(M10)
AIC(M10)

# Create pairs plot. 
ggpairs(M10_df)
ggpairs(M10_C_df)
ggpairs(M10_H_df)
```

# M11: Neural middle data + affective data + behavioral data
```{r, echo = FALSE}
M11 <- lm(log(Gross_US_M1) ~ Type 
             + scale(Theaters_US_M1)
             #+ Total_weeks 
             #+ Weeks_avg_per_theater
             + scale(Pos_arousal_scaled) 
             + scale(Neg_arousal_scaled)  
             #+ scale(W_score_scaled) 
             + scale(NAcc_middle) 
             + scale(AIns_middle) 
             + scale(MPFC_middle) 
             + Type:scale(Theaters_US_M1)
             + Type:scale(Pos_arousal_scaled)
             + Type:scale(Neg_arousal_scaled)
             + Type:scale(NAcc_middle) 
             + Type:scale(AIns_middle) 
             + Type:scale(MPFC_middle)
             , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M11)
r.squaredGLMM(M11)
AIC(M11)

# Create pairs plot. 
ggpairs(M11_df)
ggpairs(M11_C_df)
ggpairs(M11_H_df)
```

# M12: Neural offset data alone
```{r, echo = FALSE}
M12 <- lm(log(Gross_US_M1) ~ Type + 
              + scale(NAcc_offset) 
              + scale(AIns_offset) 
              + scale(MPFC_offset) 
              + Type:scale(NAcc_offset) 
              + Type:scale(AIns_offset) 
              + Type:scale(MPFC_offset) 
              , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M12)
r.squaredGLMM(M12)
AIC(M12)

# Create pairs plot. 
ggpairs(M12_df)
ggpairs(M12_C_df)
ggpairs(M12_H_df)
```

# M13: Neural offset data + affective data + behavioral data
```{r, echo = FALSE}
M13 <- lm(log(Gross_US_M1) ~ Type 
             + scale(Theaters_US_M1)
             #+ Total_weeks 
             #+ Weeks_avg_per_theater
             + scale(Pos_arousal_scaled) 
             + scale(Neg_arousal_scaled)  
             #+ scale(W_score_scaled) 
             + scale(NAcc_offset) 
             + scale(AIns_offset) 
             + scale(MPFC_offset) 
             + Type:scale(Theaters_US_M1)
             + Type:scale(Pos_arousal_scaled)
             + Type:scale(Neg_arousal_scaled)
             + Type:scale(NAcc_offset) 
             + Type:scale(AIns_offset) 
             + Type:scale(MPFC_offset)
             , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M13)
r.squaredGLMM(M13)
AIC(M13)

# Create pairs plot. 
ggpairs(M13_df)
ggpairs(M13_C_df)
ggpairs(M13_H_df)
```

# M14: Sequence Model
```{r, echo = FALSE}
M14 <- lm(log(Gross_US_M1) ~ Type 
             + scale(Theaters_US_M1)
             #+ Total_weeks 
             #+ Weeks_avg_per_theater
             #+ scale(Pos_arousal_scaled) 
             #+ scale(Neg_arousal_scaled)  
             #+ scale(W_score_scaled) 
             + scale(NAcc_onset) 
             + scale(AIns_middle) 
             + scale(MPFC_offset) 
             #+ Type:scale(Theaters_US_M1)
             #+ Type:scale(Pos_arousal_scaled)
             #+ Type:scale(Neg_arousal_scaled)
             + Type:scale(NAcc_onset) 
             + Type:scale(AIns_middle) 
             + Type:scale(MPFC_offset)
             , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M14)
r.squaredGLMM(M14)
AIC(M14)
```

# M15: Sequence Model 2
```{r, echo = FALSE}
 # Effects become more significant if we remove 'Theater_num' predictor... we can do that with the 
# 'GrossOverTheaters' variable, however MPFC looks a bit funny.  
M15 <- lm(log(Gross_US_M1) ~ Type
             + scale(Theaters_US_M1)
             #+ Weeks_avg_per_theater
             + scale(Pos_arousal_scaled) 
             + scale(Neg_arousal_scaled)  
             + scale(NAcc_onset) 
             + scale(AIns_middle) 
             + scale(MPFC_offset) 
             + Type:scale(Theaters_US_M1) # Should we have a theaters interaction? 
             + Type:scale(Pos_arousal_scaled)
             + Type:scale(Neg_arousal_scaled)
             + Type:scale(NAcc_onset) 
             + Type:scale(AIns_middle) 
             + Type:scale(MPFC_offset)
             , data = AllSubs_NeuralActivation %>% mutate(Type = factor(Type, levels = c("horror", "comedy"))))
summary(M15)
r.squaredGLMM(M15)
AIC(M15)

# Create pairs plot. 
ggpairs(M14_df)
ggpairs(M14_C_df)
ggpairs(M14_H_df)
```